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Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Endmember Independence and Spatial Weighted Abundance

Hyperspectral image unmixing is an important task for remote sensing image processing. It aims at decomposing the mixed pixel of the image to identify a set of constituent materials called endmembers and to obtain their proportions named abundances. Recently, number of algorithms based on sparse non...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Jingyan Zhang, Xiangrong Zhang, Licheng Jiao
Format: Artigo
Sprache:Inglês
Veröffentlicht: MDPI AG 2021-06-01
Schriftenreihe:Remote Sensing
Schlagworte:
hyperspectral image
spectral unmixing
endmember independence
spatial weight
manifold learning
Online-Zugang:https://www.mdpi.com/2072-4292/13/12/2348
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